Bibliographic record
Abstract
Dear Readers, I know many of you have attempted to engage reluctant young readers with good books, particularly young male readers. The Globe and Mail published an article on this dilemma entitled “Hey kids, reading novels will make you rich (and other lies worth telling)” on October 3, 2011, written by Russell Smith in response to a provocative article in the Toronto Star that claimed: “The sustained reading of many pages of text is quickly becoming obsolete, like Latin.” Indeed, according to Michael Reist, a high-school English teacher with 30 years of experience, the study of Latin died a slow death because it could no longer be used for anything, and he concludes that, “the reading of literature in school is dying the same slow death”. I encourage you to read these thought-provoking articles because they raise important questions about reading as a life skill and whether it is being diminished as students spend more time in the “three-minute world” of instant online gratification. There is little doubt that today’s students are raised in a world where they have innumerable entertainment opportunities in cyberspace, but does this really mean that “big books” are dead? Certainly, authors of children’s and young adult books are acutely aware that they need to develop highly engaging stories to hold the attention of readers, particularly young male readers, but they also know that what attracts young people to video games and other forms of online entertainment is the art of storytelling. In this issue there are plenty of recommended books with great stories to engage young readers (especially boys!), including delightful board books (e.g., My Dad is the Best Playground), fast-paced mysteries (e.g., The Money Pit Mystery), and fun character-driven stories (e.g., Cheesie Mack Is Not a Genius or Anything), to name but a few. I can think of numerous reasons to keep literature alive in schools but young people will never get hooked on reading if books are not made available to them from an early age. We need to get excellent books in the hands of young readers on a regular basis. I have already identified half a dozen books from this issue that I will be adding to my holiday gift list for the young people in my life. Happy reading!Robert DesmaraisManaging Editor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".